Concept

Coarse-grained modeling

Publications associées (65)

Unsupervised machine learning in atomistic simulations, between predictions and understanding

Automated analyses of the outcome of a simulation have been an important part of atomistic modeling since the early days, addressing the need of linking the behavior of individual atoms and the collective properties that are usually the final quantity of i ...

2019

Entropy production for coarse-grained dynamics

Daniel Maria Busiello, Amos Maritan

Systems out of equilibrium exhibit a net production of entropy. We study the dynamics of a stochastic system represented by a Master equation (ME) that can be modeled by a Fokker-Planck equation in a coarse-grained, mesoscopic description. We show that the ...

2019

Efficient conversion of chemical energy into mechanical work by Hsp70 chaperones

Paolo De Los Rios, Alessandro Barducci, Salvatore Assenza, Alberto Stefano Sassi

Hsp70 molecular chaperones are abundant ATP-dependent nanomachines that actively reshape non-native, misfolded proteins and assist a wide variety of essential cellular processes. Here, we combine complementary theoretical approaches to elucidate the struct ...

2019

Modeling the Structural and Thermal Properties of Loaded Metal–Organic Frameworks. An Interplay of Quantum and Anharmonic Fluctuations

Michele Ceriotti, Venkat Kapil

Metal–organic frameworks show both fundamental interest and great promise for applications in adsorption-based technologies, such as the separation and storage of gases. The flexibility and complexity of the molecular scaffold pose a considerable challenge ...

2019

MiMiC: A Novel Framework for Multiscale Modeling in Computational Chemistry

Ursula Röthlisberger, Simone Meloni, Martin Peter Bircher, Viacheslav Bolnykh, Paolo Carloni

We present a flexible and efficient framework for multiscale modeling in computational chemistry (MiMiC). It is based on a multiple-program multiple-data (MPMD) model with loosely coupled programs. Fast data exchange between programs is achieved through th ...

AMER CHEMICAL SOC2019

Adhesive wear mechanisms uncovered by atomistic simulations

Jean-François Molinari, Enrico Milanese, Ramin Aghababaei, Tobias Brink, Lucas Henri Galilée Frérot

In this review, we discuss our recent advances in modeling adhesive wear mechanisms using coarse-grained atomistic simulations. In particular, we present how a model pair potential reveals the transition from ductile shearing of an asperity to the formatio ...

SPRINGEROPEN2018

Statistical Analysis of Protein Sequences: A Coevolutionary Study of Molecular Chaperones

Duccio Malinverni

Recent advances in DNA sequencing technologies led to the accumulation of enormous quantities of genetic information available in public databases. This rapid growth of available biological datasets calls for quantitative analysis tools and concomitantly o ...

EPFL2018

Studying membrane-protein interplay by modeling realistic conditions

Martina Audagnotto

Realistic models of cellular environments have long captured the imagination of biologists and physical scientists. More than 20 years ago, Goodshellâs inspiring rendering of cellular environments provided the foundation for the idea of capturing the ful ...

EPFL2017

Modeling Hsp70/Hsp40 interaction by multi-scale molecular simulations and co-evolutionary sequence analysis

Paolo De Los Rios, Alessandro Barducci, Duccio Malinverni

The interaction between the Heat Shock Proteins 70 and 40 is at the core of the ATPase regulation of the chaperone machinery that maintains protein homeostasis. However, the structural details of the interaction are still elusive and contrasting models hav ...

Elife Sciences Publications Ltd2017

On heterogeneous coupling of multiscale methods for problems with and without scale separation

Assyr Abdulle

In this paper we discuss partial differential equations with multiple scales for which scale resolution are needed in some subregions, while a separation of scale and numerical homogenization is possible in the remaining part of the computational domain. D ...

2017

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